# A tibble: 571 × 51,017
survived ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457
<lgl> <dbl> <dbl> <dbl> <dbl>
1 TRUE -0.434 -0.123 -0.526 -0.330
2 TRUE -0.322 -0.123 -1.10 0.900
3 TRUE -0.479 -0.123 0.0255 -0.737
4 TRUE -0.299 -0.123 -0.452 -0.336
5 TRUE 0.240 -0.123 -0.992 0.696
6 FALSE -0.434 -0.123 -1.25 -1.22
7 TRUE 0.689 -0.123 -0.757 -0.586
8 TRUE -0.389 -0.123 -0.927 0.0561
9 TRUE -0.479 -0.123 -1.09 0.582
10 FALSE -0.479 -0.123 -0.755 -0.796
# ℹ 561 more rows
# ℹ 51,012 more variables: ENSG00000000460 <dbl>, ENSG00000000938 <dbl>,
# ENSG00000000971 <dbl>, ENSG00000001036 <dbl>, ENSG00000001084 <dbl>,
# ENSG00000001167 <dbl>, ENSG00000001460 <dbl>, ENSG00000001461 <dbl>,
# ENSG00000001497 <dbl>, ENSG00000001561 <dbl>, ENSG00000001617 <dbl>,
# ENSG00000001626 <dbl>, ENSG00000001629 <dbl>, ENSG00000001630 <dbl>,
# ENSG00000001631 <dbl>, ENSG00000002016 <dbl>, ENSG00000002079 <dbl>, …Transcription to Prediction
Predicting Survival in AML Patients from RNASeq Data using SVM
Methodology
SVM - First Try
- Short introduction to SVM:
- A supervised machine learning algorithm that can be used for classification or regression tasks.
- It maps data ot a higher dimension space and finds the hyperplane that best separates the data into different classes.
- It can be used for both linear and non-linear classification tasks using the kernel trick.
- We fit a default SVM model using the linear kernel and either the whole dataset or the 60 important features.
- Feature selection is effective.
Methodology
SVM - \(c\)-values & Non-linear Models
We need to tune the \(c\) value for the SVM algorithm.
- A low \(c\) value allows for a wider margin but more misclassification.
- A high \(c\) value allows for a narrower margin but less misclassification.
In additional to the linear kernel, we also tried two non-linear kernels:
The gaussian kernel: \[ K(x, x') = \exp\left(-\frac{\|x - x'\|^2}{\epsilon}\right) \]
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The Gaussian Kernel
The polynomial kernel.
\[ K(x, x') = (<x, x'> + C)^d \]
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The Polynomial Kernel
Methodology
Model and Hyperparameter Tuning (cont’d)5
flowchart LR boruta(Boruta Feature Selection) --> svm(SVM) svm --> c_values(Choose C Values) c_values --> linear_svm(Linear SVM) c_values --> gaussian_svm(Gaussian SVM) c_values --> polynomial_svm(Polynomial SVM) gaussian_svm --> hyper_gaussian(Choose Epsilon) polynomial_svm --> hyper_polynomial(Choose Degree and Constant) linear_svm ---> model_evaluation(MCC) hyper_gaussian --> model_evaluation hyper_polynomial --> model_evaluation
Results
SVM - Training the Final Model
- We still can do something to improve the model (in terms of increasing the prediction power).
- We have limited number of samples, so we can conserve them by using a 10-fold cross-validation instead of the 80/20 split we’ve been using.
- K-fold is also more robust than the 80/20 split by evaluating the model on different subsets of the data.
